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\section{Related Work}
\label{sec:related}

There have been several studies that extract tables from the Web. \cite{gatterbauer2006table} extract tables 
from Web pages based on their DOM nodes' visual positions. \cite{cafarella2008uncovering} proposed the 
initial WebTables vision for web-scale table extraction, implementing a mix of hand-written detectors and 
statistical classifiers that identified $154$ million high-quality relational style tables from a raw 
collection of 14.1 billion tables on the Web. \cite{elmeleegy2009harvesting} split lists on Web pages into 
multi-column tables in a domain-independent and unsupervised manner. 

Some previous works tried to discover and leverage the relationship between individual tables. 
\cite{pimplikar12} answers a query from millions of tables by identifying a table's relevance to the query 
and mapping the columns of relevant tables to the query's key words. Table synthesis is also connected to 
finding related tables and yet very different. The latter is only concerned with 
finding tables that are relevant in some way, such as being about the same topic, even when those tables are 
entirely not stitchable.

The Octopus System~\cite{cafarella2009data} includes a context operator that tries to identify additional 
information about the table on a Web page. The operator was implemented by ranking a keyword list according 
to the Tf/Idf scores. Our work differs in that our extracted attribute values are represented as meaningful 
phrases instead of isolated keywords. Moreover, we align them in columns, providing a structured view across 
the tables instead of orderless lists of keywords.

Lastly, Multiple Sequence Alignment is originally designed to compare DNA 
sequences~\cite{gusfield1997algorithms}. It has been also used in other areas.
For example, \cite{barzilay2002bootstrapping,barzilay2003learning} applied MSA to lexicon acquisition for a 
text generation system and to find paraphrasing patterns. Instead of being given two sequences of fixed 
symbols for alignment, we need to determine the segments of tokens on the fly and simultaneously align those 
segments. For that, the classic sequence alignment algorithm is extended for our scenario by incorporating 
candidate segments from various heuristics.
